AB0825 A solution proposal to the intraobserver disagreement problem in the evaluation of sacroiliac joint radiography: ‘‘Image Processing Method’’
Authors: A. C. Genç, F. Turkoglu Genc, D. Karatas, Z. N. Kaya, L. Rada, E. Gönüllü
Journal: Annals of the Rheumatic Diseases (2022)

Background Ankylosing spondylitis (AS) is a rheumatological disease that causes low back pain. The diagnosis of ankylosing spondylitis is delayed for an average of 8 years due to the insidious course of the disease. Late diagnosis of AS can cause functional and physical disabilities. Radiographic sacroiliitis can be graded in the range of 0-4 and these classes may not be sharply separated from each other. Interpretation of the sacroiliac joint radiography may differ from physician to physician. In fact, the same physician may interpret it differently at different times (1). The reason for such confusion is the presence of blur or illumination and contrast problems leading to difficulties to detect pathological areas. This paper has developed a simple image processing technique that adjusts the contrast of such images by modifying their intensity distribution using the histogram information. This transformation gives a linear trend to the cumulative probability function associated with the image and makes the images better for human vision. The common characteristic of low contrast images is the concentration of the histogram values of the image intensity near a narrow range (2). Objectives Changing the contrast range into a wider range changes the image value distribution that leads to better image vision. Methods Rheumatologist scored 260 x-ray scans grade 0 to 4. One day later, the same rheumatologist scored again the same scans randomly. It has been noticed that most of the answers were closed to the first-day decision but not all of them. We processed all the x-ray images with histogram equalization. Then the rheumatologist scored the scans randomly and answered two times in two consecutive days. To compare the agreement in radiography interpretation Kappa test was used. We use IBM SPSS 21 for the statistical analysis (kappa test) and Matlab R2021a for the image processing part. Results Severe improvement before and after the image processing application by increasing the Kappa value from 0.55 (p&lt;0.001) [moderate agreement] to 0.71(p&lt;0.001) [substantial agreement] which is a substantial agreement. Conclusion Multidisciplinary approach can be helpful in solving the intraobserver disagreement problem. The use of image processing techniques can be used for the benefit of the patient in radiographs whose standardization is difficult due to multifactorial reasons. The histogram equalization method was found to be statistically significant in finding the optimum contrast for interpreting the sacroiliac joint radiography. The output of this work indicates and motivates our team into deeper research and more analyses on how to further improve our results. Figure 1. The original graph on the left, the classical histogram equalization method on the right and their histogram tables. References [1]van den Berg, R. et al. Agreement between clinical practice and trained central reading in reading of sacroiliac joints on plain pelvic radiographs. Results from the DESIR cohort. Arthritis Rheumatol 66, 2403–2411 (2014). [2]Gu, K., Zhai, G., Wang, S., Liu, M., Zhoi, J., &amp; Lin, W. (2015). A general histogram modification framework for efficient contrast enhancement. In 2015 IEEE International Symposium on Circuits and Systems (ISCAS). Disclosure of Interests None declared